Nishant Kejriwal
Papers
7
Total Citations
137
H-Index
5
About
Nishant Kejriwal is a robotics researcher whose work spans autonomous mobile robotics, computer vision, and practical robot deployment in real-world environments. His most influential contribution is in the domain of simultaneous localization and mapping (SLAM), where his 2015 paper on loop closure detection introduced a novel bag of word pairs approach that significantly reduces perceptual aliasing — a persistent challenge in topological mapping — earning 64 citations and establishing him as a notable voice in robot navigation research. Kejriwal has also made meaningful strides in retail robotics, developing image-based product counting systems using monocular cameras and pioneering virtual reality-assisted frameworks for autonomous retail stock monitoring — work that speaks to the growing intersection of mobile robotics and commercial automation. His 2015 product counting paper has garnered 38 citations, reflecting strong interest from both academic and industry communities. Beyond navigation and perception, Kejriwal has contributed to fleet management via cloud robotics platforms, human-following robot systems leveraging SURF-based visual tracking, and service robots such as an office tea-serving robot. Across these diverse applications, his research consistently bridges theoretical algorithms with tangible real-world deployments, making his body of work particularly relevant for students exploring applied and service robotics.
Research Focus
Key Achievements
Top Papers
- 1High performance loop closure detection using bag of word pairs64 citations · 2015
- 2
- 3Remote retail monitoring and stock assessment using mobile robots18 citations · 2014
- 4
- 5SURF-based human tracking algorithm for a human-following mobile robot6 citations · 2015
- 6
- 7A Tea-Serving Robot for Office Environment2 citations · 2014